The vagueness and uncertainty of data is a frequent problem in marketing research. Since rough sets have already proven their usefulness in dealing with such data in other domains like medicine (Ohrn 1999) and image processing (Pawlak et al. 1995), the question arises, whether they are a useful alternative to the more popular fuzzy sets for marketing research as well. In the present (more technical) paper we discuss the pros and cons of the LEM2 (Learning from Examples Module Version 2) algorithm in this respect. Beyond that its efficiency will be demonstrated by solving two marketing-related classification problems. The main objective is to show that the LEM2 algorithm is an adequate tool for marketing research which deserves much more attention as it is the case so far. Therefore, we also investigate the reasons for the obvious ignoring of rough sets in the marketing literature to date. The implementation of the LEM2 algorithm, which we use in the empirical part of our paper, is part of the data mining system LERS (Learning from Examples based on Rough Sets) which was introduced in the early nineties by Grzymala-Busse (1992) and modified in 1997 by the same author. It generates the smallest set of minimal rules of a decision class by means of rough sets. Therefore, as we will show, the LEM2 algorithm is suited to efficiently generate classification rules for decision making in (new) customer selection for instance.